A study of
the techniques and processes used in business forecasting.Primary emphasis on univariate time series.Techniques studied include simple smoothing methods, decomposition methods,
Box-Jenkins ARIMA methods and regression.Prerequisites:Math 140 or its equivalent and DIS 220 or its
equivalent or instructors permission.

University Studies Approval is
requested in: Flag
Requirements:Mathematics and Statistics

Attachments: The syllabus explains what are
typically covered in this course and addresses the coverage for the two outcomes for
mathematics and statistics (a-b).The syllabus is included in this application for
purposes of illustration.Each faculty member
is still responsible for his/her own course syllabus.Examples of assignments to students are also included in this application.They illustrate some of the learning activities
that students undertake aside from exams and in-class work.

As required
by the approval process, the following address the two outcomes listed for Mathematics and
Statistics flag:

a. practice the correct application of
mathematical or statistical models that are appropriate to their prerequisite knowledge of
those areas

In order to generate a useful
forecast, one must typically decompose the data into a possible trend effect, a possible
seasonal effect, and a possible cyclical effect.Students
learn to look at a data series, identify its time series properties (trend, seasonal and
cyclical effects, or weak stationarity) with graphical and statistical analysis (including
hypothesis testing).Then, they learn to
apply the appropriate forecasting technique(s), given the goals of the forecast.

b. make
proper use of modern mathematical or statistical methods appropriate to their level of
prerequisite knowledge, to include, if statistics is used in a substantive way, the use of
a statistical package with graphics capability when appropriate.

Multivariate regression analysis and ARIMA modeling are too complex for students to
hand calculate the results.Moreover, most
students lack the mathematical sophistication to program Excel to generate the
desired results.Consequently, successful
completion of almost any assignment requires a student use either JMP or SPSS.

In addition, one of the foci of this course is to get students to visually analyze
their results.They must plot or graph the
forecasts and model fits in order to find outliers and to help in choosing the best model.

Students are required to analyze the residuals (unexplained portion) of statistical
forecasting models.Besides visually checking
for randomness, students test for randomness and normality in the residuals.

Finally, when comparing different forecasting models, students perform hypothesis
tests on the forecasting models, use statistics to test the forecasting properties.Students compare plots of the different
forecasts.

FIN 335-
FORECASTING METHODS

Professor

e-mail:

Office Hours Posted on my office
door.They are subject to change during the
semester.

Class Hours: MWF 8-8:50PM, 11-11:50PM,
12-12:50PM, and1-1: 50PM.I am
usually in my office ten or so minutes before class as well.There may be occasional deviations from this
weekly schedule.

Each student in the course will 1) gain a rudimentary knowledge of several common
forecasting techniques, 2) learn how to evaluate the accuracy of a forecasting model, 3)
gain an appreciation of the importance of data quality, and 4) observe and encounter some
of the common problems in building a forecasting model.

UNIVERSITY STUDIES

University policy requires the
following information for your benefit.Finance
335 fulfills the 3 s.h. of a Mathematics/Statistics Flag course required in the University
Studies Program.As such, this courses seeks to

a. practice the correct application of
mathematical or statistical models that are appropriate to their prerequisite knowledge of
those areas; and

b. make proper use of modern mathematical or
statistical methods appropriate to their level of prerequisite knowledge, to include, if
statistics is used in a substantive way, the use of a statistical package with graphics
capability when appropriate.

Letters in parenthesis below identify
these outcomes.

DETERMINATION OF FINAL GRADES

Each student's course grade depends on
the student's point total from exams (48%), assignments (48%) and class participation.There are 3exams, each graded on 100 points.Various assignments given out in class.At the end of the semester, the assignment scores
are totaled and scaled to 100 basis.There is no deviation from this procedure - no extra
credit is given.

Assignments (a, b)

Students receive homework assignments in class.Students may work in groups of no more than three.Assignments are to be turned in or postmarked by the due date.All assignments are due on the date
indicated on the assignment:late assignments
lose 1/3 of the total value for each day it is late.

Exams (a, b)

Exams consist of definitions, short answer and short problems.Each exam covers part of the course.The first exam covers Chapters 1-4. The second
exam covers chapters 4-8.The final exam
covers Chapters 9 and parts of Chapters 10, 11 and 12.

Reading Assignments (a, b)

Students should keep up with the reading assignments.Lecture topics do not cover all of the text.All students are capable of understanding much of
the reading assignments without assistance from the instructor.Lectures are a combination of explanations
of important / difficult points and extensions of the text.Reading the assigned material by the scheduled date is very important.

Computer Software (a, b)

Much of the data manipulation can be accomplished in most spreadsheet programs,
including Excel.Basic regression tools are
usually accessible in spreadsheets.However,
for some of the time series analysis, students need access to a software package that
performs ARIMA models.There are three
such packages available on campus at this time:JMP,
SASS, and SPSS.Any student with a new laptop
has JMP on it.For students with older
laptops, the IT lab on the second floor of Somsen will load JMP or SASS onto your machine.SPSS is available in the computer lab on the
second floor of Somsen and the third floor of Somsen.

Students should choose the package that is easiest for them to use.Each package has advantages and disadvantages.In class, I alternate among the packages.

TOPIC AND READINGSCHEDULE:

WEEKSTOPICREADING

PART IGetting
Started

1-5Introduction
and Review (a)Chapters 1-2

Primer
on Regression(a)Appendix to 1

Visual
Inspection of Data(a,b)Chapter 3

Trends(a,b)Chapter 4

1st Exam over Part I

PART IIModeling
Time Series

6-10Seasonality(a,b)Chapter 5

Modeling
Cycles( a,b)Chapters 6 and 7

Forecasting CyclesChapter 8

2nd Exam over Part II

PART IIIForecasting

11-15Trends, Seasonality and Cycles(a,b)Chapter 9

RegressionModels(a,b)Chapter 10:pages 241-250, 259-276

Evaluating Forecasts(a)Chapter 11:pages 287-294

Stochastic Trends, Unit Roots(a.b)Chapter 12:pages 323-352

3rd
(Final) Examover Part III

FIN 335 1st Assignment (a, b)

1. (2 points) Describe the distribution of the daily noon Swedish
Krone to U.S. dollar exchange rate, include a histogram.

2.(2 points) Test at
the 99% confidence level, whether its distribution is normal.

3.(4 points) In the
previous year and a half, the average exchange rate was 9.68 Krone per U.S. dollar with a
standard deviation of 0.753.

a.Test at the 95%
confidence level, if the exchange rate has increased from 9.68 Krone per U.S. $.

b.Test at the 95%
confidence level if the standard deviation is different from 0.753 Krone per U.S. $.

6. (2 points) Looking at your answers to questions 1-5 what
conclusions (not statistical ones) can one draw about the monthly forecasts over these two
years?

FIN 335 3rd Assignment (a, b)

Estimate the following model:PRIMEt=
B0
+B1
FFRt + et

where PRIME is the monthly prime interest rate, and FFR is the monthly federal
funds rate,

using the data below from 1997.01 through 2001.12.

1.(1 points) Write
estimated equation.

2.(2 points) Does the
model fit the data?Explain by interpreting
the R2.

3.(2 points) Test at
the 99% confidence level, whether the intercept is statistically different from 0.

4.(2 points) Interpret
the slope coefficient.

5.(2 points) Test at
the 95% confidence level, whether the slope is statistically different from 1.

6.(2 points) Test at
the 95% confidence level whether there is 1st order autocorrelation present.

7.(2 points) Write the
estimated model with autocorrelation present.

FIN 335 4th Assignment (a, b)

1.(6 points) Using data
from 1959 through 1989, estimate the following three trend functions  linear,
polynomial, and growth  for real personal consumption expenditures per capital (in
1996 dollars).

2.(6 points) Graph the
predicted values for each trend function and the actual values over time for that period.Graph the residuals over time for each trend
function.After looking at each graph, and
comparing the measures of goodness of fit for each trend function, explain which trend
function appears to fit the data the best and which one appears to fit the worst.

3.(6 points) Use each
trend function to perform an in-sample forecast for 1990 to 2000.

5.(6 points) Graph the
predicted values for each trend function and the actual values.After looking at that graph, and comparing the
goodness of fit measures, explain which trend function appears to forecast the best, and
which one appears to forecast the worst.

2.(6 points) Graph the
predicted values for each trend function and the actual values over time for that period.Graph the residuals over time for each trend
function.After looking at each graph, and
comparing the measures of goodness of fit for each trend function, explain which trend
function appears to fit the data the best and which one appears to fit the worst.Choose at least 2 trend functions to use.

3.(8 points)
Re-estimate the trend functions over the same period with dummy variables for seasonal
adjustment.Analyze the results, and, if you
have to, re-estimate the equations with only the statistically significant dummies.

4.(6 points) Graph the
predicted values for each trend function and the actual values over time for that period.Graph the residuals over time for each function.After looking at each graph, and comparing the
measures of goodness of fit for each trend function, explain which trend function appears
to fit the data the best and which one appears to fit the worst.Choose at least 2 trend functions to use.

5. (6 points) Use the remaining functions to perform an in-sample
forecast for 1997 and 1998.

6.(10 points) For each
function compute measures of forecast goodness of fits and decompose its MSE.

7.(6 points) Graph the
predicted values for each trend function and the actual values.After looking at that graph, and comparing the
goodness of fit measures, explain which trend function appears to forecast the best, and
which one appears to forecast the worst.

FIN 335 6th Assignment (a)

1.(3 points) Rewrite
the following equations, using backshift operators

a. (1 point) 0.87yt + 0.65yt-1 + 0.44yt-2
+0.31yt-3 +0.11yt-4

b.(2 points) 0.54D2yt + 0.04 D2yt-1- 0.26 D2yt-2

2. (3 points) Rewrite the following equations without backshift
operators

a.(1 point) (1 
0.45L+ 0.13L2- 0.37L3+ 0.28L4) yt

b.(2 points) (1 +
0.67L+ 0.26L2)(1-L) yt

3.(4 points) Rewrite
and simplify (if possible) the following equations using backshift operators